• January 2012
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NASA: Prize Money a Bargain for Better Software.

  Government Computer News (01/09/12) William Jackson

 Researchers at the U.S. National Aeronautics and Space Administration (NASA) and the Harvard Business School in 2010 launched the NASA Tournament Lab, an online platform for contests between independent programmers who compete to create software and algorithms and solve computational problems.  “We’re always looking at ways to fill gaps in our technical capabilities,” says NASA’s Jason Crusan.  The researchers use the Tournament Lab to order a program or algorithm for a relatively small amount of prize money.  The first challenge presented in the Tournament Lab was developing an algorithm to optimize the contents of the medical kits that go with astronauts on missions.  NASA developed specifications and 516 programmers worked on the problem.  A total of $1,000 in prize money was awarded to the top five performers in each group.  The best submission was more effective than NASA’s previous algorithm by a factor of three, and NASA is still using it today.  “We didn’t think we would have as high a success rate as we’ve had,” Crusan says.  “There are a lot of smart people in the world.”  NASA also has used crowdsourcing for a way to identify, characterize, and count lunar craters in NASA images.


Chinese Crunch Human Genome With Videogame Chips.

Wired News (01/06/12) Eric Smalley

BGI, a Chinese supercomputer lab, recently switched to servers that use graphics processing units (GPUs) built by NVIDIA, which enabled it to cut its genome analysis time by more than an order of magnitude. The feat that enabled BGI and NVIDIA to accomplish this was porting key genome analysis tools to NVIDIA’s GPU architecture, a nontrivial accomplishment that the open source community and others have been working toward, says the Jackson Laboratory’s Gregg TeHennepe. With GPUs, BGI gets faster results for its existing algorithms or it can use more sensitive algorithms to achieve better results, says bioinformatics consultant Martin Gollery. In addition, GPUs can be used to analyze genomes that could allow researchers studying biology and drug development to better treat patients. “The researcher now no longer has to own a sequencer or a cluster, and does not have to have employees to manage both of these technologies,” Gollery says. GPU-enabled cloud services will be useful once the data is in the cloud, and cloud service providers are increasingly adding GPU capabilities. Another advantage of GPU-enabled cloud services is that research organizations can test GPU versions of algorithms without having to have a GPU system in-house, Gollery notes.